Automatic detection of microaneurysms in RGB retinal fundus images

In this study, an efficient and fast-working method to detect microaneurysm lesions, first symptom of diabetic retinopathy, is described. The proposed method is based on mathematical morphology, object pixel classification and connected component analysis. The proposed algorithm responses in 4.8 seconds for 2048x1536 pixel images. This shows this system runs faster than other microaneurysm detection systems. The sensitivity and specificity of this system is 69.1% and 99.3% specificity, respectively

A study on sex estimation by using machine learning algorithms with parameters obtained from computerized tomography images of the cranium

The aim of this study is to test whether sex prediction can be made by using machine learning algorithms (ML) with parameters taken from computerized tomography (CT) images of cranium and mandible skeleton which are known to be dimorphic. CT images of the cranium skeletons of 150 men and 150 women were included in the study. 25 parameters determined were tested with different ML algorithms. Accuracy (Acc), Specificity (Spe), Sensitivity (Sen), F1 score (F1), Matthews correlation coefficient (Mcc) values were included as performance criteria and Minitab 17 package program was used in descriptive statistical analyses. p <= 0.05 value was considered as statistically significant. In ML algorithms, the highest prediction was found with 0.90 Acc, 0.80 Mcc, 0.90 Spe, 0.90 Sen, 0.90 F1 values as a result of LR algorithms. As a result of confusion matrix, it was found that 27 of 30 males and 27 of 30 females were predicted correctly. Acc ratios of other MLs were found to be between 0.81 and 0.88. It has been concluded that the LR algorithm to be applied to the parameters obtained from CT images of the cranium skeleton will predict sex with high accuracy

Gender prediction with the parameters obtained from pelvis computed tomography images and machine learning algorithms

Introduction: In the skeletal system, the most dimorphic bones employed for postmortem gender prediction include the bones in the pelvic skeleton. Bone measurements are usually conducted with cadaver bones. Computed tomography (CT) is an increasingly popular method due to its ease of use, reconstruction opportunities, and lower impact of age bias and provides a modern data source. Even when parameters obtained with different or same bones are missing, machine learning (ML) algorithms allow the use of statistical methods to predict gender. This study was carried out in order to obtain high accuracy in estimating gender with the pelvis skeleton by integrating ML algorithms, which are used extensively in the field of engineering, in the field of health. Material and Methods: In the present study, pelvic CT images of 300 healthy individuals (150 females, 150 males) between the ages of 25 and 50 (the mean female age = 40, the mean male age = 37) were transformed into orthogonal images, and landmarks were placed on promontory, iliac crest, sacroiliac joint, anterior superior iliac spine, anterior inferior iliac spine, terminal line, obturator foramen, greater trochanter, lesser trochanter, femoral head, femoral neck, body of femur, ischial tuberosity, acetabulum, and pubic symphysis, and coordinates of these regions were obtained. Four groups were formed based on various angle and length combinations obtained from these coordinates. These four groups were analyzed with ML algorithms such as Logistic Regression, Linear Discriminant Analysis (LDA), Random Forest, Extra Trees Classifier, and ADA Boost Classifier. Results: In the analysis, it was determined that the highest accuracy was 0.96 (sensitivity 0.95, specificity 0.97, Matthew's Correlation Coefficient 0.93) with LDA. Discussion and Conclusion: The use of length and angle measurements obtained from the pelvis showed that the LDA model was effective in estimating gender

Sekazu: an integrated solution tool for gender determination based on machine learning models

Gender determination is the first stage of identification used in forensic investigation, anthropology, archeology, and bioarchaeology, which helps accelerate the process of narrowing possible matches in a medical-legal context. Without DNA analysis, the dimorphic property of bones comprises a basis for gender determination with measurements taken on only bones. In this work, 9 different bones such as cranium, mandibula, femur, patella, calcaneus, condylus occipitalis, sternum, hand bones, and foot bones were used for gender determination. Machine learning methods and artificial neural networks, especially linear and quadratic discriminant analysis, while determining the gender, machine learning also were technically adopted. 13 different machine learning algorithms were used as a model for gender determination. Many tools were designed to perform processes like designing necessary bookmarks to try models, designing measurements where machine learning algorithms are used as features, determining coordinates of designed bookmarks, and computation of features. A software named Sekazu was developed by presenting an integrated solution proposal. Thanks to the developed software, models used in gender determination were developed and tried in a fast way and researchers can obtain results reported based on performance metrics flexibly. [Med-Science 2021; 10(2.000): 367-73

Gender prediction with parameters obtained from pelvis computed tomography images and decision tree algorithm

Gender prediction is among the most critical topics in forensic medicine and anthropology since it is the basis of identity (height, weight, ancestry, age). Today, osteometry which is a low-cost, easily accessible method that requires no expertise is preferred when compared to DNA technology, which has several disadvantages such as high cost, accessibility, laboratory facilities, and expert personnel requirements. The Computed Tomography (CT) method, which is little affected by orientation and provides reconstruction opportunities, was selected instead of traditional methods for osteometry. This study aims to predict high and accurate gender with the Decision Tree (DT) algorithms used in the field of health recently. In the present study, CT images of 300 individuals (150 females, 150 males) without a pathology on the pelvic skeleton and between the ages of 25 and 50 were transformed into orthogonal form, landmarks were placed on promontorium, sacroiliac joint, iliac crest, terminal line, anterior superior iliac spine, anterior inferior iliac spine, greater trochanter, obturator foramen, lesser trochanter, femoral head, femoral neck, the body of femur, ischial tuberosity, acetabulum, and pubic symphysis, and the coordinates of these landmarks were determined. Then, parameters such as angle and length were obtained with various combinations. These parameters were analyzed with the DT algorithm.The analysis conducted with the DT algorithm revealed that accuracy (Acc) was 0.93, sensitivity was 0.95, specificity was 0.90, and the Matthews correlation coefficient was 0.86 for the pelvic skeleton. It was observed that the accuracy was quite high and more realistic when determined with the DT algorithm. In conclusion, the DT algorithm with multiple parameters and samples on pelvic CT images could improve the Acc of gender prediction. [Med-Science 2021; 10(2.000): 356-61

Comparison of pirfenidone and corticosteroid treatments at the COVID-19 pneumonia with the guide of artificial intelligence supported thoracic computed tomography

Aim We aimed to investigate the effect of short-term pirfenidone treatment on prolonged COVID-19 pneumonia. Method Hospital files of patients hospitalised with a diagnosis of critical COVID-19 pneumonia from November 2020 to March 2021 were retrospectively reviewed. Chest computed tomography images taken both before treatment and 2 months after treatment, demographic characteristics and laboratory parameters of patients receiving pirfenidone + methylprednisolone (n = 13) and only methylprednisolones (n = 9) were recorded. Pulmonary function tests were performed after the second month of the treatment. CT involvement rates were determined by machine learning. Results A total of 22 patients, 13 of whom (59.1%) were using methylprednisolone + pirfenidone and 9 of whom (40.9%) were using only methylprednisolone were included. When the blood gas parameters and pulmonary function tests of the patients were compared at the end of the second month, it was found that the FEV1, FEV1%, FVC and FVC% values were statistically significantly higher in the methylprednisolone + pirfenidone group compared with the methylprednisolone group (P = .025, P = .012, P = .026 and P = .017, respectively). When the rates of change in CT scans at diagnosis and second month of treatment were examined, it was found that the involvement rates in the methylprednisolone + pirfenidone group were statistically significantly decreased (P < .001). Conclusion Antifibrotic agents can reduce fibrosis that may develop in the future. These can also help dose reduction and/or non-use strategy for methylprednisolone therapy, which has many side effects. Further large series and randomised controlled studies are needed on this subject.WOS:0007077954000012-s2.0-85117142372PubMed: 3462415

A study on the correlation between spleen volume estimated via cavalieri principle on computed tomography images with basic hemogram and biochemical blood parameters

Considering its hematological and immunological functions, spleen is a very important organ. A change occurs in its size as the spleen performs these functions. This study aims to examine the possible relationships between spleen volume and the basic hemogram and biochemical parameters in serum. Multidetector computed tomography images and basic hemogram and biochemical parameters of 74 adult individuals, 34 male and 40 female, who were found to be healthy, were used in the study. Spleen volume was estimated using the Cavalieri method on multidetector computed tomography images and the correlations between the volume value with basic hemogram and biochemistry parameters were researched. While negative significant correlations were found between the estimated spleen volume and lymphocyte percentage (r=-0.224) and platelet level (r=-0.271); positive significant correlations were found between hemoglobin level (r=0.228), hematocrit level (r=0.237), alanine aminotransferase (r=0.345), and erythrocyte level (r=0.375). As a result of this study, a relationship was found between spleen volume and lymphocyte percentage, hematocrit level, erythrocyte level, platelet level, and alanine aminotransferase level in serum. We believe that the results of the study will provide a larger perspective to clinicians in the diagnosis of diseases associated with spleen

Disappearance of Biodiversity and Future of Our Foods

“I. Uluslararası Organik Tarım ve Biyoçeşitlilik Sempozyumu 27-29 Eylül Bayburt